I will summarize this feature here, add some links and annotations and, well, venture a few of my own questions. Also, I believe this is a logical follow to three previous Subway Fold posts about the music biz including:

In mid-2017, the music tech market is generating signals as to its direction and viability. For example, Jawbone, the once thriving manufacturer of wearable audio devices is currently being liquidated; Soundcloud the audio distribution platform let go of 40 percent of its staff recently only days before the firm’s tenth anniversary; and Pandora has experienced high turnover among its executives while seeking a sale.

Nonetheless, the leaders in music streaming are maintaining “the music industry’s growth”. Music tech showcases and music accelerators including SXSW Music Startup Spotlight, the Midemlab Accelerator, and Techstars Music are likewise driving market transformation. During 2017 thus far, 54 music startups from more than 25 cities across the globe have taken part in these three entities. They have presented a range of submissions including “live music activations and automated messaging to analytics tools for labels and artists”.

While companies such as Live Nation, Balderton Capital and Evolution Media have previously invested in music startups, most investors at this mid-year point have never previously funded a company in this space. This is despite the fact that investments in this market sector have rarely returned the 30% that VCs generally seek. As well, a number of established music industry stars are participating as first-time or veteran investors this year.

Of the almost $900 million funding in music tech for the first half of this year, 75% was allocated for streaming services – – 82% of which went only to the leading four companies. However, there remains a “stark disconnect” involving the types of situations where music accelerators principally “lend their mentorship” in “hardware, virtual reality1, chatbots, label tools”, and the issues that VC concentrate the funding such as “streaming, social media, brands”. Moreover, this situation has the potential of “stifling innovation” across the industry.

To date, music accelerators have “successfully given a platform and resources” to some sectors of the industry that VCs don’t often consider. For example, automated messaging and AI-generated music2 are both categories that music accelerators avoided until recently, now equal 15% of membership. This expansion into new categories reflects a much deeper “tech investment and hiring trends”. Leading music companies are now optimistic about virtual digital assistants (VDA) including chatbots and voice-activated systems such as Amazon Alexa3. As well, Spotify recently hired away a leading AI expert from Sony.

Rhythm

However, this “egalitarian focus” on significant problems has failed to “translate into the wider investing landscape” insofar as the streaming services have attracted 75% of music tech funding. The data further shows that licensing/rights/catalog management, social music media, and music, brands and advertising finished, in that order, in second at 11.1%, third at 7.1% and fourth at 3.9%.

These percentages closely match those for 2016. Currently, many VCs in this sector view streaming “as the safest model available”. It is also one upon which today’s music industry depends for its survival.

Turning to the number of rounds of music tech funding rather than the dollar amounts raised, by segments within the industry, a “slightly more egalitarian landscape” emerges:

Music hardware, AI-generated music, and VR and Immersive media each at 5.0%

Live music; music brands and advertising; streaming; and social music media each at 15.0%

Categories that did relatively well in both their number of rounds of funding and accelerator membership were “catalog management, social music platforms, and live music”.

Those music tech startups that are more “futuristic” like hardware and VR are seen favorably by “accelerators and conference audiences”, but less so among VCs. Likewise, while corporate giants including Live Nation, Universal Music Group, Citi and Microsoft have announced movement into music VR in the past six months, VC funding for this tech remained “relatively soft”.

Even more pronounced is the situation where musical artists and label services such as Instrumental (a influencer discovery platform) and chart monitors like Soundcharts have not raised any rounds of funding. This is so “despite unmatched attention from accelerators. This might be due to these services not being large enough to draw too “many traditional investors”.

An even more persistent problem here is that not many VCs “are run by people with experience in the music industry” and are familiar with its particular concerns. Once exception is Plus Eight Equity Partners, who are trying to address “this ideological and motivational gap”.

Then there are startups such as 8tracks and Chew who are “experimenting with crowdfunding” in this arena but who were not figured into this analysis.

In conclusion, the tension between a “gap in industry knowledge” and the VCs’ preference for “safety and convenience”, is blurring the line leading from accelerator to investment for many of these imaginative startups.

My Questions

Of those music startups who have successfully raised funding, what factors distinguished their winning pitches and presentations that others can learn from and apply?

Do VCs and accelerators really need the insights and advice of music industry professionals or are the numbers, projects and ROIs only what really matters in deciding whether or not to provide support?

Would the application of Moneyball principles be useful to VCs and accelerators in their decision-making processes?

A remarkable moment in modern advertizing occurred during the 2013 Super Bowl when the power temporarily went out at the Superdome stadium in New Orleans. Oreo cookies quickly put out a tweet with an accompanying photo that read “Power Out? No problem. You can still dunk in the dark.” It has since been widely heralded as a spontaneous stroke of genius and proved to be incredibly effective across the Twitter-verse. The story of how this happened, including the actual tweet and graphic, were told in a concise report on CNET.com entitled How Oreo’s Brilliant Blackout Tweet Won the Super Bowl by Daniel Terdiman on February 3, 2013. The story was widely reported elsewhere in traditional and social media venues.

Two years later, the 2015 Super Bowl itself ended in incredible drama. Neither any sports writer nor the NFL itself could have scripted a more improbable ending. Discussions of the final minute of the game broke out instantly across social and traditional and will likely continue on for years afterwards.

According to the report on SocialMediaToday.com, the virtual “water cooler discussions” that occurred on Twitter around the advertisers’ hashtags embedded in their TV ads showed that users are now employing multiple screens to experience the game (the television screen and then at least one other computing device’s screen). These additional screens can be used to track and analyze the value per ad dollars spent while the advertisers evaluate their social media data in real-time rather than traditionally having to wait for TV viewing data to arrive. By further adding specialized demographic data into the mix, the advertisers can thus more deeply assess their data, scaling from in the aggregate level all the way down to the individual level. This gives advertisers the opportunity to observe and assess individuals “interacting with their brand” and pinpoint the “influencers” on Twitter among them. Furthermore, they can overlay an additional layer of data onto their contemporaneous hashtag analyses by using prior Twitter exchanges involving their audience in an effort to illuminate “brand affinity, preferences, and attitude changes over time”.

My questions are as follows:

What calculations and considerations are used when advertisers select their hashtags for advertising on TV and other media? Does Brand X use one hashtag for a certain media platform and/or audience than they do for another? Does Brand Y in the same market sector use a similar or different approach?

How, if at all, do geographic factors affect the choice of advertising hashtags? Will viewers and readers from one area of the US respond differently than another area to the same hashtag? Is there any difference in hashtag strategy from country to country or does the global nature of TV and the social media eliminate such considerations?

If a particular hashtag worked well for 2015’s Super Bowl, should it automatically be re-used for next year’s game or should marketing and content strategists re-evaluate their hashtag formulation and selection process?

Are advertising hashtags usually devised by a company’s internal marketing and analytics staff members or do they more often engage outside consultants for assistance with this?

[This post was originally uploaded on September 26, 2014. It has been updated below with new information on February 5, 2015.]

Have you ever wondered what a visual map of your Twitter network might look like? The realization of such Twitter topography was covered in a terrific post on September 24, 2014 on socialmediatoday.com entitled How to Create a Visual Map of Your Twitter Network by Mary Ellen Egan.

To briefly sum up, at the recent Social Shake-Up Conference in Atlanta sponsored by SocialMediaToday, the Social Research Foundation created and presented such a map. They generated it by including 513 Twitter users who participated for four days in the hashtag #socialshakeup. The platform used is called NodeXL. The resulting graphic of the results as shown in this article are extraordinary. Please pay particular attention as to how the “influencers” in this network are identified and their characteristics. I strongly urge you to click through to read this article and see this display.

I believe this article and report will quite likely spark your imagination. I think it is safe to assume that many users would be intrigued by this capability and, moreover, would devise new and innovative ways to leverage the data to better understand, grow and plot strategy to enhance their Twitter networks. Some questions I propose for such an analysis while inspecting a Twitter map include:

Am I reaching my target audience? Is this map reliable as a sole indicator or should others be used?

Who are the key influencers in my network? Once identified, can it be determined why they are influencers?

Does my growth strategy depend on promoting retweets, growing the population of followers, getting mentioned in relevant publications and websites, or other possible approaches?

What I would really be like to see emerge is a 3-dimensional form of visual map that fully integrates multiple maps of an individual’s or group’s or company’s online presence to simultaneously include their Twitter, Facebook, LinkedIn¹, Instagram and other social networks. Maybe a platform like the Hyve-3D visualization system² could be used to enable a more broadly extensible and scalable 3D view. Perhaps this multi-dimensional virtual construct could produce entirely new planning and insights for optimizing one’s presence, marketing and influence in social media.

If so, would new trends and influencers not previously seen then be identified? Could tools be developed in this system whereby users would test the strengths and weaknesses of certain cross-social media platforms links and relationships? Would certain industries such news networks³ be able to spot events and trends much sooner? Are there any potentially new opportunities here for entrepreneurs?

February 5, 2015 Update:

A very instructive and illuminating example of the power of mapping a specialized Twitter network has just been posted by Ryan Whelan, a law and doctoral student at Northwestern University. It is composed of US law school professors who are now actively Tweeting away. He posted his methodology, an interactive graphic of this network, and one supporting graph plus four data tables on his blog in a February 3, 2015 post entitled The Law Prof Twitter Network 2.0. I highly recommend clicking through and reading this in its entirety. Try clicking on the graphic to activate a set of tools to explore and query this network map. As well, the tables illustrate the relative sensitivities of the data and their impact on the graphic when particular members of the network or the origins and groupings of the followers is examined.

I think you find it inspiring in thinking about what situations such a network map might be helpful to you in work, school, special interest groups, and many other potential applications. Mr. Whelan presents plenty of information to get you started off in the right direction.

I also found the look and feel of the network map to be very similar to the network mapping tool that was previously available on LinkedIn and discussed in the August 14, 2014 Subway Fold post entitled 2014 LinkedIn Usage Trends and Additional Data Questions.

My questions are as follows:

What effects, if any, is this network and its structure having upon improving the legal education system? That is, are these professors, by being active on Twitter in their own handle and as members of this network as followers of each other, benefiting the professor’s work and/or law students’ classroom and learning experiences?

Are the characteristics of this network of legal academics any different from, let’s say, a Twitter network of medical school professors or high school teachers?

Would more of a meta-study of networks within the legal profession produce results that would be helpful to lawyers and their clients? For example, what would Twitter maps of corporate lawyers, litigators and public interest attorneys show that might be helpful and to whom?

3. See also a most interesting article published in the September 23, 2014 edition of The New York Times entitled Tool Called Dataminr Hunts for News in the Din of Twitter by Leslie Kaufman about such a system that is scanning and interpolating possible news emerging from the Twitter-sphere.